Another feasible technique is to add noise to the training data.
Neural networks can be generalized, i.e., to avoid overfitting, by utilizing non-physical noisy measurement, since the test data can be generally regarded as `noisy' data against the training data [1]}.
We can increase the training data infinitely such that,
\(n_{\rm noise} = \gamma n_{\rm DNS},\ \gamma =2,3,4,\cdots ,\) 
